A method for analyzing industrial time series data based on modular configuration

Abstract

The present invention discloses an industrial time series data parsing method based on modular configuration, which aims to separate the time series data definition and parsing, and realize efficient parsing through the time series data configuration file. The method is implemented by the following steps: S10, the production line end collects sensor data, converts it into the original message containing the timestamp, the number of sensors and the ID, and parses the configuration file to generate configuration information containing the workstation action sequence. S20, parses the original message to generate a timestamp and a sensor ID list, and matches it to the sensor cache of each workstation. S30, each workstation matches the cache data according to the configuration information to determine the actual execution action sequence, and stores it in the execution sequence cache. S40, combines the execution sequences of each workstation according to the workstation execution order, and obtains the complete action sequence and timestamp of the product in the production line process. The method of the present invention is configurable, can adapt to different industrial production needs, and realizes efficient and accurate time series data parsing.

Description

Technical Field

[0001] The present invention belongs to the field of industrial time series data analysis, and in particular relates to a modularly configured industrial time series data analysis method. Background Art

[0002] In modern industrial production, the widespread use of devices such as sensors and actuators generates a large amount of time series data. This data records various states and actions in the production process and is of great significance for monitoring production processes, detecting anomalies, and optimizing production efficiency. However, the parsing and processing of industrial time series data often face challenges such as large and complex data volumes, diverse data sources, high real-time requirements, and high parsing complexity. Existing industrial time series data parsing methods often have the following problems: Traditional data parsing methods are usually based on a single parsing strategy and cannot cope with diverse data sources and complex data structures, resulting in low parsing efficiency. Parsing methods are usually fixed and difficult to flexibly adjust and optimize according to different production environments and needs. In addition, they lack effective matching algorithms and optimization strategies.

[0003] In view of the shortcomings of existing technologies, a new and efficient industrial time series data analysis technology is urgently needed to solve the above problems. Summary of the Invention

[0004] The present invention proposes an industrial time series data analysis method based on modular configuration, comprising the following steps:

[0005] S10: Insert an acquisition program at the production line end to collect sensor information and convert it into industrial time series data raw messages; the industrial time series data raw messages are composed of several raw message data points, and the format of the raw message data points includes a timestamp T, the number of triggered sensors N, and the IDs of the N sensors; obtain and parse the time series data configuration file to generate workstation configuration information in the form of a structure; the action sequence is an array composed of the sensor IDs of several sensors related to executing an action sequence, arranged in order, and each workstation has one or more action sequences;

[0006] S20: parse the original message of industrial time series data to obtain a list L consisting of timestamps and corresponding sensor IDs.

[0007] Match the data in list L with each workstation according to the sensor ID number and store them in the sensor cache of the corresponding workstation; the sensor cache includes a sensor ID cache and a timestamp cache;

[0008] S30: For each workstation, obtain all action sequences of the workstation from the workstation configuration information, match the action sequences with the workstation sensor cache one by one, obtain the action sequence executed by the workstation based on the matching results, and store it in the execution sequence cache of the workstation;

[0009] S40: According to the station execution order defined in the station configuration information, the execution sequences of different stations are read in turn for combination and splicing, and the action sequence and timestamp of a product passing through all stations in the production line process are obtained as the analysis result.

[0010] Preferably, the execution process of the acquisition program in S10 includes the following steps:

[0011] (1) Assign a unique identifier ID to each sensor;

[0012] (2) Perform data acquisition operations at a predetermined period Δt; at the beginning of each period, record the current timestamp T;

[0013] In the current cycle, record all triggered sensor IDs to form a list V;

[0014] (3) If the list V is not empty and contains N sensor IDs, the collected data is encapsulated into a data packet;

[0015] The format of the data packet is: {timestamp T, number of triggered sensors N, sensor ID1, sensor ID2, ...,

[0016] Sensor ID n}.

[0017] Preferably, the time series data configuration file in S10 includes the following contents: a unique number of the workstation, a name of the workstation, a list of action sequences of the workstation, and a sequential execution order relationship between the workstations.

[0018] Preferably, the workstation configuration information in the structural form of S10 includes the following contents: the workstation unique number unique_id, the data comes from the unique number of the workstation in the timing data configuration file, the workstation name pos_name, the data comes from the name of the workstation in the timing data configuration file, the action sequence list possible_exc_seqs, the data comes from the action sequence list of the workstation in the timing data configuration file, the order template_info of the workstation in the production line process, the action sequence list length possible_exc_seqs_length, used to record the length of the action sequence list possible_exc_seqs, the workstation sensor ID cache fifo_id, the workstation timestamp cache fifo_time, and the parsed execution sequence cache fifo_excute_seq of the workstation.

[0019] Preferably, step S20 includes the following sub-steps:

[0020] S201: Parse the original industrial time series data message sent by the acquisition program, extract the sensor ID and timestamp contained in each original message data point, and encapsulate each original message data point into a key-value pair {timestamp, sensor ID} to construct a list L;

[0021] S202: Construct a hash table H based on the workstation configuration information in S10, where the key of the hash table H is the sensor ID and the value is the workstation ID that requires the sensor data; this hash table is used for subsequent data distribution;

[0022] S203: Based on the hash table H constructed in S202, the list L is traversed. For each key-value pair {timestamp, sensor ID} in the list L, the hash table H is searched to see whether there is a workstation with the sensor ID of the key-value pair as the key; if so, the sensor ID of the key-value pair is added to the workstation sensor ID cache fifo_id of the corresponding workstation, and the timestamp of the key-value pair is added to the timestamp cache fifo_time of the corresponding workstation; if not, the key-value pair is discarded.

[0023] Preferably, step S30 includes the following sub-steps:

[0024] S301: Based on the sensor ID cache fifo_id and timestamp cache fifo_time of the current workstation, a time series data list received by the current workstation is constructed; each element of the time series list is a key-value pair {timestamp, sensor ID}, and the action sequence list possible_exc_seqs of the current workstation is obtained from the workstation configuration information;

[0025] S302: Traverse each action sequence in the action sequence list possible_exc_seqs of the current workstation; for each action sequence, perform the following matching operations:

[0026] a. Match the current action sequence with the time series data list received by the workstation. The matching rule is: check whether the sensor IDs in the action sequence exist in the time series data list in order. The timestamp is not a necessary condition for matching, but is used to associate subsequent results.

[0027] b. If yes, record the unique identifier of the action sequence that matched successfully, and extract the timestamp list corresponding to the sensor ID in the action sequence from the time series data list; combine the unique identifier of the action sequence and the corresponding timestamp list into a result item; for example, {action sequence identifier: X, timestamp list: [T1, T2, T3, ...]}.

[0028] S303: All result items of the successfully matched action sequences are stored in the execution sequence cache fifo_excute_seq of the workstation;

[0029] Preferably, step S40 includes the following sub-steps:

[0030] S401: Obtaining the sequence template_info of all workstations in the production line process from the workstation configuration information to form a global execution sequence between workstations; the global execution sequence between workstations refers to the order in which a product passes through all workstations in the production line process;

[0031] S402: Traverse each workstation in sequence according to the execution order between the global workstations. For the currently traversed workstation, read the action sequence result item with the smallest timestamp from its execution sequence cache fifo_excute_seq and add it to the product analysis result list. The product analysis result list is a list of action sequences and timestamps of all workstations a product passes through in the production line process.

[0032] S403: After all workstations are traversed, the final product parsing result list will contain the action sequences of all workstations arranged in the order of execution and their corresponding timestamp information;

[0033] S404: Convert the format of the final product analysis result list for subsequent analysis or use.

[0034] Compared with existing technologies, this invention completely separates the definition of time series data (through configuration files) from the parsing process, and uses configuration files in TOML format to describe production line and workstation information, achieving a high degree of flexibility and configurability. By modifying the configuration files, users can easily adapt to different production environments, adjust the order of workstations, add or delete workstations and sensors, modify action sequences, etc., without modifying the code, greatly simplifying system configuration and maintenance and reducing deployment costs. This modular configuration method enables the invention to be quickly deployed to different production lines, with good versatility and scalability.

[0035] During the data preprocessing phase, this paper introduces a hash table to maintain the mapping between sensor IDs and workstation IDs. Leveraging the hash table's O(1) average search time complexity, this method accelerates data filtering and distribution, effectively reducing the time required for data preprocessing and significantly outperforming traditional linear search methods.

[0036] Each parsing step of the present invention (configuration file parsing, data preprocessing, action sequence matching, and product parsing result generation) is relatively independent and highly modular. This modular design makes the system structure clear, easy to understand, and maintain, and facilitates subsequent functional expansion and upgrades. For example, new data processing modules can be easily added, matching algorithms can be modified, and output formats can be expanded without affecting other parts of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the specific embodiments or the description of the prior art. Hereinafter, some specific embodiments of the present invention will be described in detail in an illustrative and non-limiting manner with reference to the drawings. The same reference numerals in the drawings indicate the same or similar components or parts. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings:

[0038] Figure 1 This is a flowchart of the present invention

[0039] Figure 2 The original time series data format and conversion diagram of the present invention

[0040] Figure 3 Schematic diagram of data flow of each module of the present invention DETAILED DESCRIPTION

[0041] The following is a further description of the technical solution provided by the present invention in conjunction with the accompanying drawings. The following examples are only used to more clearly illustrate the technical solution of the present invention and are not intended to limit the scope of protection of the present invention.

[0042] The present invention proposes an industrial time series data analysis method based on modular configuration, the flow chart is as follows Figure 1 The steps in the flowchart are further described below:

[0043] S10: Insert the acquisition program at the production line end to collect sensor information and convert it into industrial time series data raw messages. Obtain the time series data configuration file and parse it to generate the workstation configuration information in the form of a structure. The following is the specific structure of the workstation information structure:

[0044]

[0045]

[0046] The core of this step is to use a time series data configuration file (TOML language is used in this invention) to describe the production process of a production line, so as to realize the digitization and standardization of production line information. A production line usually consists of one or more workstations, each of which performs one or more action sequences. Each sensor is assigned a unique ID number. Through the time series data configuration file, complex production processes can be clearly expressed, and subsequent parsing and processing are facilitated. In the embodiment, the time series data configuration file is as follows:

[0047] [line]

[0048] name="Main Production Line"

[0049] id=1

[0050] [line.workstations.workstation1]

[0051] name="Assembly Station"

[0052] id=1

[0053] action_sequences=[[101,102,103],[104,105],

[106] ]

[0054] [line.workstations.workstation2]

[0055] name="Packaging Station"

[0056] id=2

[0057] action_sequences=[[201,202],[203,204],[205,206,207]]

[0058] [line.workstation_order]

[0059] order=[1,2]

[0060] The functions of the main fields of the above time series data configuration file are as follows: [line] represents the root node of the production line, where the name field defines the name of the production line, which is "Main Production Line" in this case, and the id field defines the unique identifier of the production line, which is 1 in this case. [line.workstations.workstation1] represents the first workstation in the production line. The name field defines the name of the workstation, which is "Assembly Station" in this case, and the id field defines the unique identifier of the workstation, which is 1 in this case. The action_sequences field defines the action sequence array of the workstation. Each action sequence is an array consisting of sensor IDs, including three action sequences here: [101,102,103], [104,105], and

[106] . [line.workstations.workstation2] represents the second workstation in the production line. The name field defines the workstation's name, here "Packaging Station." The id field defines the workstation's unique identifier, here 2. The action_sequences field defines an array of action sequences for the workstation. Each action sequence is an array of action IDs. Here, there are three action sequences: [201,202], [203,204], and [205,206,207]. The order in [line.workstation_order] indicates the execution order between the workstations on the production line. [1,2] means that workstation 1 executes first, followed by workstation 2. This time series data configuration file describes a production line named "Main Production Line" with an ID of 1. This line contains two workstations: "Assembly Station" and "Packaging Station," with IDs 1 and 2, respectively. Each workstation has one or more action sequences, represented by an array of sensor IDs. The TOML-decoded line configuration file can be parsed to obtain the workstation configuration information.

[0061] S20: parse the original message of industrial time series data, obtain a list L consisting of timestamps and corresponding sensor IDs, match them according to the sensor ID number, match the data in list L with each workstation and store them in the sensor cache of the corresponding workstation. The data stream conversion process is as follows: Figure 3 The detailed steps are as follows:

[0062] S201: Receive and parse the original industrial time series data message sent by the acquisition program, extract the sensor ID and timestamp contained in each original message data point, and encapsulate each original message data point into a key-value pair {timestamp, sensor ID} to construct a list L. The original industrial time series data message format is: {timestamp T, number of triggered sensors N, sensor ID1, sensor ID2, ..., sensor ID N}, and the specific conversion process is as follows: Figure 2 For example, if the received industrial time series raw data message is: {1635678900,3,101,102,103}, the parsed list L is:

[0063] [{1635678900,101},{1635678900,102},{1635678900,103}], if two consecutive messages {1678886400,5,201,202,203,204,205} and {1678886401,2,206,207} are received, the list L generated after parsing is: [{1678886400,201},{1678886400,202},{1678886400,203},

[0064] {1678886400,204},{1678886400,205},{1678886401,206},{1678886401,207}].

[0065] S202: Based on the workstation configuration information parsed in step S10, a hash table H is constructed to maintain a mapping relationship between sensor IDs and workstation IDs that require sensor data. The key of the hash table is the sensor ID, and the value is the workstation ID that requires the sensor data. This hash table is used for subsequent data distribution. Assume that the workstation configuration information parsed in step S10 is as follows:

[0066] Station 1 (Assembly Station) requires sensor IDs: 101, 102, 103;

[0067] Station 2 (Packaging Station) requires sensor IDs: 201, 203, 206.

[0068] Then the constructed hash table H is {H

[101] =[1],H

[102] =[1],H

[103] =[1],H

[201] =[2],H

[202] =[2],H

[203] =[2]}.

[0069] S203: Perform data distribution and filtering based on the hash table constructed in S202, traverse the list L, and for each key-value pair {timestamp, sensor ID} in the list, perform the following operations:

[0070] 1. Check in hash table H whether there is a workstation with the sensor ID of the key-value pair as the key.

[0071] 2. If it exists, add the sensor ID of the data point to the fifo_id cache of the corresponding station, and add the timestamp of the key-value pair to the fifo_time cache of the corresponding station.

[0072] 3. If the mapping for the sensor ID does not exist in the hash table, the key-value pair is discarded.

[0073] Assume there is a list L:

[0074] [{1678886400,101},{1678886400,103},{1678886400,105},{1678886402,102},{1678886402,201},{1678886405,203},{1678886405,202},{1678886405,207},{1678886405,101},{1678886406,300}; the hash table H is {H

[101] =[1],H

[102] =[1],H

[103] =[1],H

[201] =

[0075] [2], H

[202] = [2], H

[203] = [2]}, the execution results are as follows:

[0076] Workstation 1 fifo_time: [1678886400,1678886400,1678886402,1678886405]

[0077] Workstation 1 fifo_id: [101,103,102,101]

[0078] Station 2 fifo_time: [1678886402,1678886405,1678886405]

[0079] Workstation 2 fifo_id: [201, 203, 202]

[0080] S30: For each workstation, all action sequences of the workstation are obtained from the workstation configuration information, and the action sequences are matched one by one with the workstation sensor cache. The action sequence executed by the workstation is obtained according to the matching results, and the results are stored in the execution sequence cache of the workstation. The data flow conversion process is as follows: Figure 3The specific steps are as follows:

[0081] S301: Build a list of time series data received by the current workstation from the sensor ID cache fifo_id and timestamp cache fifo_time of the current workstation. Each element of the list is a key-value pair {timestamp, sensor ID}. Obtain the action sequence list possible_exc_seqs of the current workstation from the workstation configuration information.

[0082] S302: Traverse each action sequence in the action sequence list possible_exc_seqs of the current workstation. For each action sequence, perform the following matching operations:

[0083] a. Match the current action sequence (consisting of a series of sensor IDs) with the time series data list received by the workstation. The matching rule is to check whether the sensor IDs in the action sequence exist in the time series data list in the correct order. The timestamp is not a necessary condition for matching, but is used for subsequent result correlation.

[0084] b. If the match is successful (i.e. all sensor IDs in the action sequence appear in the time series data list in order

[0085] ), record the unique identifier of the successfully matched action sequence and extract the timestamp list corresponding to the sensor ID in the action sequence from the time series data list. Combine the action sequence identifier and the corresponding timestamp list into a result item, for example, {action sequence identifier: X, timestamp list: [T1, T2, T3, ...]}.

[0086] S303: Store all successfully matched action sequence result items (including action sequence identifiers and corresponding timestamp lists) into the execution sequence cache fifo_excute_seq of the workstation;

[0087] Specifically, suppose there is a workstation with the following data in its sensor cache: [{16001,101},{16003,102},{16005,104},{16007,103}], where the first item of each data tuple is the timestamp and the second item is the sensor ID.

[0088] There are two action sequences that can be executed at this station, defined as follows: Sequence 1: [101, 102], Sequence 2: [104, 103]. Extracting actions from the sensor cache and trying to match them against the above action sequence yields:

[0089] Action sequence 1 matches the actions {16001, 101} and {16003, 102} in the sensor cache, which conforms to the definition of sequence 1. Therefore, the action sequence executed by the workstation is confirmed to be sequence 1, and the corresponding timestamp is extracted as [16001, 16003]. Action sequence 2 matches the actions {16005, 104} and {16007, 103} in the sensor cache, which conforms to the definition of sequence 2. Therefore, the action sequence executed by the workstation is confirmed to be sequence 2, and the corresponding timestamp is extracted as [16005, 16007]. The parsing results can be listed as follows:

[0090]

[0091] This result shows that the workstation has executed two action sequences, Sequence 1 and Sequence 2, in sequence. The timestamps corresponding to Sequence 1 are [16001, 16003], and the timestamps corresponding to Sequence 2 are [16005, 16007].

[0092] S40: According to the station execution order defined in the station configuration information, read the execution sequences of different stations in turn and combine them to obtain the action sequence and timestamp of a product passing through all stations in the production line process as the parsing result. The parsing result of a product refers to a list of action sequences and timestamps of the product passing through all stations in the production line process, which represents the assembly process of the product when passing through different stations. Figure 3 As shown in the figure, after completing steps S10 to S30, the action sequence of each workstation and its corresponding timestamp list have been obtained. Next, the action sequences of these workstations need to be combined according to the order of the product production process to form a complete product analysis result. The specific steps are as follows:

[0093] S401: Obtain the sequence template_info of the production line process of all workstations from the workstation configuration information to form a global execution sequence between workstations. This information defines the execution sequence of each workstation in the product production process.

[0094] S402: Traverse each workstation in turn according to the acquired workstation execution order. For the currently traversed workstation, read the first matching action sequence result from its execution sequence cache fifo_excute_seq and add it to the product analysis result list;

[0095] S403: After all workstations are traversed, the final product parsing result list will contain the action sequences of all workstations arranged in the order of execution and their corresponding timestamp information;

[0096] S404: Format or convert the final product parsing result list for subsequent analysis or use.

[0097] Assume that there is an execution sequence cache of the following stations (obtained from the result of step S30)

[0098] Station 1 (Assembly Station):

[0099]

[0100] Station 2 (Packaging Station):

[0101]

[0102] Furthermore, according to the [line.workstation_order]order=[1,2] field in the production line TOML configuration file, workstation 1 should be executed earlier than workstation 2. Therefore, by reading the execution sequence caches of workstations 1 and 2 in order, two products can be parsed: one product executes sequence 1 on the Assembly Station and action sequence 3 on the Packaging Station; the other product executes action sequence 2 on the Assembly Station and action sequence 4 on the Packaging Station.

[0103] Product 1

[0104]

[0105] Product 2

[0106]

[0107] Finally, the product analysis results are stored in a suitable data structure (in this embodiment, a CSV table is used). The storage content of product 1 is shown in the following table:

[0108] Workstation Sequence ID Timestamp 1 Timestamp 2 Assembly Station Sequence 1 16001 16003 Packaging Station Sequence 3 16010 16012

[0109] The storage contents of Product 2 are shown in the following table:

[0110] Workstation Sequence ID Timestamp 1 Timestamp 2 Assembly Station Sequence 2 16005 16007 Packaging Station Sequence 4 16015 16017

[0111] The above description is only part of the specific implementation methods of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any person familiar with the art within the technical scope disclosed in the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for analyzing industrial time series data based on modular configuration, characterized in that: The following steps are involved: S10: Inserting a collection program at the production line end to collect sensor information and convert it into an original industrial time series data message; the original industrial time series data message consists of a number of original message data points, and the format of the original message data point includes a timestamp T, the number of triggered sensors N, and the IDs of the N sensors; Obtain and parse the time series data configuration file to generate workstation configuration information in the form of a structure; An action sequence is an array consisting of the sensor IDs of several sensors related to the execution of an action sequence, arranged in order. Each workstation has one or more action sequences. S20: parse the original message of industrial time series data to obtain a list L consisting of timestamps and corresponding sensor IDs. Match the data in list L with each workstation according to the sensor ID number and store them in the sensor cache of the corresponding workstation; the sensor cache includes a sensor ID cache and a timestamp cache; S30: For each workstation, obtain all action sequences of the workstation from the workstation configuration information. Match the action sequence with the workstation sensor cache one by one, obtain the action sequence executed by the workstation based on the matching results, and store it in the execution sequence cache of the workstation; S40: According to the station execution order defined in the station configuration information, the execution sequences of different stations are read in turn for combination and splicing, and the action sequence and timestamp of a product passing through all stations in the production line process are obtained as the analysis result.

2. The industrial time series data analysis method according to claim 1, characterized in that: The execution process of the acquisition program in S10 includes the following steps: (1) Assign a unique identifier ID to each sensor; (2) Perform data acquisition operations at a predetermined period Δt; at the beginning of each period, record the current timestamp T; within the current period, record all triggered sensor IDs to form a list V; (3) If the list V is not empty and contains N sensor IDs, the collected data is encapsulated into a data packet; the format of the data packet is: {timestamp T, number of triggered sensors N, sensor ID1, sensor ID2, ..., sensor ID N }.

3. The industrial time series data analysis method according to claim 1, characterized in that: The time series data configuration file in S10 includes the following contents: a unique number of the workstation, a name of the workstation, a list of action sequences of the workstation, and a sequential execution order relationship between the workstations.

4. The industrial time series data analysis method according to claim 3, characterized in that: The workstation configuration information in the structural form of S10 includes the following contents: The unique number of the workstation, unique_id, comes from the unique number of the workstation in the time series data configuration file. The workstation name pos_name, the data comes from the name of the workstation in the time series data configuration file, The action sequence list possible_exc_seqs, the data comes from the action sequence list of the workstation in the time series data configuration file, the order of the workstation in the production line process template_info, The action sequence list length possible_exc_seqs_length is used to record the length of the action sequence list possible_exc_seqs, the workstation sensor ID cache fifo_id, the workstation timestamp cache fifo_time, and the parsed execution sequence cache fifo_excute_seq of the workstation.

5. The industrial time series data analysis method according to claim 4, characterized in that: Step S20 includes the following sub-steps: S201: Parse the original industrial time series data message sent by the acquisition program, extract the sensor ID and timestamp contained in each original message data point, and encapsulate each original message data point into a key-value pair {timestamp, sensor ID} to construct a list L; S202: Constructing a hash table H based on the workstation configuration information in S10, where the key of the hash table H is the sensor ID and the value is the workstation ID that requires the sensor data; S203: Based on the hash table H constructed in S202, the list L is traversed. For each key-value pair {timestamp, sensor ID} in the list L, the hash table H is searched to see whether there is a workstation with the sensor ID of the key-value pair as the key; if so, the sensor ID of the key-value pair is added to the workstation sensor ID cache fifo_id of the corresponding workstation, and the timestamp of the key-value pair is added to the timestamp cache fifo_time of the corresponding workstation; if not, the key-value pair is discarded.

6. The industrial time series data analysis method according to claim 4, characterized in that: Step S30 includes the following sub-steps: S301: Build a time series data list received by the current workstation based on the sensor ID cache fifo_id and the timestamp cache fifo_time of the current workstation; Each element of the time series data list is a key-value pair {timestamp, sensor ID}, and the action sequence list possible_exc_seqs of the current workstation is obtained from the workstation configuration information; S302: Traverse each action sequence in the action sequence list possible_exc_seqs of the current workstation; for each action sequence, perform the following matching operations: a. Match the current action sequence with the time series data list received by the station. The matching rule is: check whether the sensor IDs in the action sequence exist in the time series data list in order; b. If yes, record the unique identifier of the action sequence that matches successfully, and extract the timestamp list corresponding to the sensor ID in the action sequence from the time series data list; combine the unique identifier of the action sequence and the corresponding timestamp list into a result item; S303: All result items of the successfully matched action sequences are stored in the execution sequence cache fifo_excute_seq of the workstation.

7. The industrial time series data analysis method according to claim 6, characterized in that: Step S40 includes the following sub-steps: S401: Obtaining the sequence template_info of all workstations in the production line process from the workstation configuration information to form a global execution sequence between workstations; the global execution sequence between workstations refers to the order in which a product passes through all workstations in the production line process; S402: Traverse each workstation in sequence according to the execution order between the global workstations. For the currently traversed workstation, read the action sequence result item with the smallest timestamp from its execution sequence cache fifo_excute_seq and add it to the product analysis result list. The product analysis result list is a list of action sequences and timestamps of all workstations a product passes through in the production line process. S403: After all workstations are traversed, the final product parsing result list will contain the action sequences of all workstations arranged in the order of execution and their corresponding timestamp information; S404: Convert the format of the final product analysis result list.

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